CN113947254A - Power grid overdue asset value remodeling method, system and storage medium - Google Patents

Power grid overdue asset value remodeling method, system and storage medium Download PDF

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CN113947254A
CN113947254A CN202111246823.9A CN202111246823A CN113947254A CN 113947254 A CN113947254 A CN 113947254A CN 202111246823 A CN202111246823 A CN 202111246823A CN 113947254 A CN113947254 A CN 113947254A
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power grid
overdue
asset
service life
assets
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葛艳琴
陈英华
杨一鸣
汪亚平
徐雪松
李洪江
齐化锋
于娟英
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State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office
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State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The scheme discloses a power grid overdue asset value remodeling method, which comprises the following steps: constructing a local technical transformation dynamic planning model for the overdue assets of the power grid year by year, and determining a technical transformation scheme for the overdue assets of the power grid and a weighted average usable life of the overdue assets of the power grid after technical transformation; constructing a comprehensive evaluation index system of the health state of the overdue assets of the power grid based on the actual operation state of the current power grid equipment, and evaluating the remaining service life of the overdue assets of the power grid; and calculating the remaining service life of the power grid overdue asset after technical transformation based on a power grid overdue asset health state comprehensive evaluation index system, and comparing the remaining service life with the weighted average service life of the power grid overdue asset after technical transformation to determine the remaining service life of the power grid overdue asset after technical transformation. The method can determine the optimal technical scheme and financial depreciation age after technical modification of the transformer.

Description

Power grid overdue asset value remodeling method, system and storage medium
Technical Field
The invention relates to asset value evaluation, in particular to a method, a system and a storage medium for remodeling overdue asset value of a power grid.
Background
With the continuous increase of the scale of physical assets of the power grid, the tasks of operation, maintenance, updating and transformation of the power grid become heavier and heavier, and the working form of asset management is severe. Through researching the over-age asset value remodeling and asset wall risk coping strategies, analyzing and evaluating the implementation of technical reconstruction overhaul projects and the coping capability of power failure guarantee, determining scientific and reasonable investment scale and management strategies in advance, further optimizing the investment scale, structure and time sequence, fully considering the efficiency benefit of inventory assets, avoiding investment impulsion and blind investment and providing guarantee for the safe operation of a power grid.
Disclosure of Invention
One purpose of the scheme is to provide a power grid overdue asset value remodeling method. The method can determine the optimal technical scheme and financial depreciation age after technical modification of the transformer.
In order to achieve the purpose, the scheme is as follows:
a power grid overdue asset value remodeling method comprises the following steps:
constructing a local technical transformation dynamic planning model for the overdue assets of the power grid year by year, and determining a technical transformation scheme for the overdue assets of the power grid and a weighted average usable life of the overdue assets of the power grid after technical transformation;
constructing a comprehensive evaluation index system of the health state of the overdue assets of the power grid based on the actual operation state of the current power grid equipment, and evaluating the remaining service life of the overdue assets of the power grid;
and calculating the remaining service life of the power grid overdue asset after technical transformation based on a power grid overdue asset health state comprehensive evaluation index system, and comparing the remaining service life with the weighted average service life of the power grid overdue asset after technical transformation to determine the remaining service life of the power grid overdue asset after technical transformation.
Preferably, the dynamic planning model for local technical transformation of the aged assets of the power grid year by year is shown as a formula (1) and a formula (2):
an objective function:
Figure BDA0003321116020000021
constraint conditions are as follows:
Figure BDA0003321116020000022
in the formulas (1) and (2), X represents the predicted service life of the aged asset after technical transformation; w is aiRepresenting an investment amount of the ith technical improvement; t is tiIs a variable of 0-1 and indicates whether the technology modification is carried out for the ith time, if the technology modification is carried out for the ith time, tiWhen the ith time is not technically modified, t is 1i0; (j-i) represents the age in which the renewed modified capital was put into use relative to the estimated year; b is total investment of technical improvement; n is1Representing the actual years elapsed before the evaluation year; y isiIndicating a net worth of assets at the end of the ith year; z is a radical ofiIndicating the original asset value of the ith technical reconstruction replacement equipment; m represents the total number of planned technical modifications; n represents the completion time of the technical transformation plan; j denotes the year of evaluation.
Preferably, the building of the comprehensive evaluation index system of the health state of the overdue assets of the power grid comprises determining an evaluation index system and weights of all levels in the system based on the type of the equipment.
Preferably, each level in the system comprises a factor level, an index level and a device health status level; the factor layer weight is determined based on an analytic hierarchy process; and the weight of the index layer is determined by applying an entropy weight method based on historical data obtained by evaluating and measuring the equipment state.
Preferably, the corresponding value of the equipment health state grade is obtained based on the factor layer weight and the index layer weight and by combining the membership function.
Preferably, the corresponding value of the device health status level is obtained by equation (24),
Figure BDA0003321116020000023
in the formula (24), HI0An initial health index representing the device; HI represents the health index at the time of device evaluation; b represents an aging coefficient; t is1Indicating a completely new plant HI0The corresponding year is generally the equipment commissioning year; t is2Indicating the year corresponding to the HI to be calculated, which may be the current year or a future year.
Preferably, the obtaining of the remaining service life of the overdue asset of the power grid based on the comprehensive evaluation index system of the health state of the overdue asset of the power grid comprises:
calculating the expected life of the overdue asset of the power grid;
calculating an aging coefficient of the power grid overdue asset;
and obtaining the residual service life T' of the overdue asset of the power grid.
Preferably, the determining the remaining service life of the power grid aged asset after technical transformation comprises:
calculating a weighted average remaining useful life R of the power grid overdue asset based on the predicted useful life of the power grid overdue asset;
comparing the weighted average remaining usable life R of the power grid overdue asset with the remaining usable life T' of the power grid overdue asset, determining the remaining usable life of the power grid overdue asset after technical transformation, as shown in formula (28),
the residual service life of the overdue assets of the power grid after technical transformation is min [ T', R ] (28),
in the formula (28), R represents the weighted average remaining usable life of the power grid overdue assets after technical transformation; t' represents the remaining usable life obtained through a power grid overdue asset health state index system; if T 'is less than R, finally taking T' from the residual service life of the overdue asset of the power grid after technical transformation; otherwise, taking R.
In a second aspect, a power grid overdue asset value remodeling system is provided, which includes:
the data acquisition unit is used for acquiring various parameters of the overdue assets of the power grid subjected to technical transformation;
the data analysis unit is used for constructing a local technical transformation dynamic planning model for the power grid overdue assets year by year, and determining a technical transformation scheme for the power grid overdue assets and the weighted average usable life of the power grid overdue assets after technical transformation;
constructing a comprehensive evaluation index system of the health state of the overdue assets of the power grid based on the actual operation state of the current power grid equipment, and evaluating the remaining service life of the overdue assets of the power grid;
and calculating the remaining service life of the power grid overdue asset after technical transformation based on a power grid overdue asset health state comprehensive evaluation index system, and comparing the remaining service life with the weighted average service life of the power grid overdue asset after technical transformation to determine the remaining service life of the power grid overdue asset after technical transformation.
In a third aspect, a computer-readable storage medium is provided, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the steps of the method according to any of the above.
The scheme has the following beneficial effects:
according to the method for rebuilding the value of the overdue assets of the power grid, for the overdue assets which are arranged to be technically improved, under the condition that the total amount of technical improvement, the technical improvement times and the term of a technical improvement project are known, local technical improvement investment amount can be effectively distributed, the technical improvement time is reasonably arranged, the financial depreciation age limit of the assets after technical improvement is scientifically determined, and the maximum benefit of rebuilding the value of the overdue assets is achieved.
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In order to illustrate the implementation of the solution more clearly, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the solution, and that other drawings may be derived from these drawings by a person skilled in the art without inventive effort.
FIG. 1 is a flow chart of a method for value remodeling of an aged asset of a power grid;
FIG. 2 is a schematic diagram of a semi-trapezoidal membership function used in the evaluation of the health status of an overdue asset in a power grid;
FIG. 3 is a schematic diagram of a node of the remaining useful life time after technical modification of the prediction device in the embodiment;
fig. 4 is a schematic diagram of a system for value remodeling of an overdue asset of a power grid.
Detailed Description
Embodiments of the present solution will be described in further detail below with reference to the accompanying drawings. It is clear that the described embodiments are only a part of the embodiments of the present solution, and not an exhaustive list of all embodiments. It should be noted that, in the present embodiment, features of the embodiment and the embodiment may be combined with each other without conflict.
The terms "first," "second," and the like in the description and in the claims, and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As shown in fig. 1, a method for rebuilding the value of an overdue asset of a power grid includes:
s100, constructing a local technical transformation dynamic planning model for the overdue assets of the power grid year by year, and determining a technical transformation scheme for the overdue assets of the power grid and a weighted average usable life of the overdue assets of the power grid after technical transformation;
s200, constructing a comprehensive evaluation index system of the health state of the overdue assets of the power grid based on the actual operation state of the current power grid equipment, and evaluating the remaining service life of the overdue assets of the power grid;
s300, calculating the remaining service life of the power grid overdue asset after technical transformation based on a power grid overdue asset health state comprehensive assessment index system, comparing the remaining service life with the weighted average service life of the power grid overdue asset after technical transformation, and determining the remaining service life of the power grid overdue asset after technical transformation.
In S100, the yearly local technical reconstruction dynamic programming model is designed based on the model assumption premise, the objective function and the constraint condition:
an objective function:
Figure BDA0003321116020000051
constraint conditions are as follows:
Figure BDA0003321116020000052
wherein X represents the predicted service life of the aged asset after technical transformation; w is aiRepresenting an investment amount of the ith technical improvement; t is tiIs a variable of 0-1 and indicates whether the technology modification is carried out for the ith time, if the technology modification is carried out for the ith time, tiWhen the ith time is not technically modified, t is 1i0; (j-i) represents the age in which the renewed modified capital was put into use relative to the estimated year; b is total investment of technical improvement; n is1Representing the actual years elapsed before the evaluation year; y isiIndicating a net worth of assets at the end of the ith year; z is a radical ofiIndicating the original asset value of the ith technical reconstruction replacement equipment; m represents the total number of planned technical modifications; n represents the year of completion of the technical improvement planLimiting; j denotes the year of evaluation.
According to the objective function and the constraint condition, an optimal technical transformation scheme, namely technical transformation investment time and corresponding investment amount, can be obtained through solving. And the overall service life of the modified overdue asset is the maximum under the optimal scheme, and the weighted average service life R of the modified overdue asset is calculated.
S200 and S300 comprise:
based on the wide application of on-line monitoring and detection products, the actual measured value of the characteristic parameter of the equipment (the overdue asset of the power grid) is mastered in time during operation, maintenance and overhaul, and after single data information of the equipment (the overdue asset of the power grid) is mastered, the measured data of the equipment can be converted into state quantity reflecting the operation condition of the equipment through a membership function of an index system comprehensively evaluated based on the health state of the overdue asset of the power grid, so that the health index of the equipment is calculated, and the residual service life of the equipment (the overdue asset of the power grid) is predicted.
Firstly, constructing a power grid overdue asset health evaluation index system, and respectively determining the weight of a factor layer and the weight of an index layer by utilizing an analytic hierarchy process and an entropy weight process; then calculating the probability of the equipment in different health state grades by combining the membership function; and finally, obtaining the remaining service life of the equipment by using a health index calculation formula. The method is used for evaluating the residual service life of the equipment after the equipment is technically improved, and the residual service life of the equipment after the equipment is finally determined by combining the corrected weighted average service life.
The method comprises the following specific steps: construction of comprehensive evaluation index system of equipment health state
1. Construction of comprehensive evaluation index system
In order to accurately evaluate the health state of the power grid equipment, a reasonable evaluation index system needs to be determined according to the type of the equipment. The equipment evaluation indexes can be formulated by combining equipment state evaluation guide rules released by national power grid companies and actual operation experiences, so that the health state of equipment (power grid overdue assets) can be comprehensively measured by various indexes. For example, refer to guidance such as overhead transmission line state evaluation guidance (Q/GDW 1173-2014), oil-immersed transformer state evaluation guidance (Q/GDW 10169-2016), and so onAn evaluation index system is constructed. According to the guiding rule, the state level L of the equipment is divided into 4 levels, namely normal, attention, abnormal and serious, and the L is expressed as L ═ L in a set1,L2,L3,L4Normal, attention, abnormal, severe. The established comprehensive evaluation index system is shown in table 1.
TABLE 1
Figure BDA0003321116020000071
2. Determining per-level weights
Determining the weight of a factor layer by using an analytic hierarchy process according to the practical use characteristics and evaluation state guide rules of the power grid equipment; and determining the weight of the index layer by applying an entropy weight method based on historical data obtained by evaluating and measuring the equipment state. The analytic hierarchy process represents that expert opinions have subjectivity, the entropy weight method reflects the characteristics of data and has objectivity, and comprehensive weights obtained by combining the two weighting methods are more reliable.
2.1 subjective weighting based on analytic hierarchy Process
An Analytic Hierarchy Process (AHP) is a qualitative and quantitative combined multi-target decision analysis method, which splits a complex problem into elements and establishes a hierarchical structure with certain logic according to a domination relationship. And quantifying the judgment of the experts by using the relative scale of pairwise comparison, determining the relative importance of each element in the same layer, and obtaining the weight of each element through the solution and consistency check of the judgment matrix.
2.1.1 construction of the decision matrix
The analytic hierarchy process needs to construct a judgment matrix, and the judgment matrix is used for comparing the relative importance of the factor layer elements under the constraint condition of a target layer in the hierarchical model. And comparing the factor layer elements in the comprehensive evaluation model with the target layer in pairs according to the importance degree to form a judgment matrix. When any two factor layer elements are compared, a certain scale is needed for measurement, according to a research conclusion that the psychologist provides that the limit capability of distinguishing information levels of people is 7 +/-2, when the analytic hierarchy process measures the relative importance degree of evaluation indexes, a judgment matrix is formed by adopting a 1-9 scale evaluation set according to the meanings given by the evaluation set, and the specific meanings of the judgment matrix are shown in a table 2.
TABLE 2
Scale Means of
1 Two elements are equally important
3 The former element being slightly more important than the latter element
5 The former element being significantly more important than the latter element
7 The former element is more important than the latter element
9 The former element being absolutely more important than the latter element
2，4，6，8 Median of the above two scales
Suppose that the target layer A and n in the factor layer1An element
Figure BDA0003321116020000086
In connection, the constructed judgment matrix is shown as formula (3):
Figure BDA0003321116020000081
in the formula (3), bijDenotes for A, fiTo fjNumerical representation of relative importance. bijJudging whether the elements in the matrix B satisfy by using a 1-9 scale method
Figure BDA0003321116020000082
In particular, when i ═ j, bij＝1。
2.1.2 factor layer weight calculation and consistency check
The index single rank order weight is a weight value calculated according to the judgment matrix, wherein the weight value is relative importance order of the elements related to the current rank for the corresponding elements of the upper rank. According to n1An element
Figure BDA0003321116020000087
For the judgment matrix B of the element A in the target layer, solving the relative weight vector of the judgment matrix B and the element A
Figure BDA0003321116020000083
The specific calculation process is as follows:
a. calculating the product M of each row of elements of the judgment matrixiNamely:
Figure BDA0003321116020000084
b. calculating MiN of (A) to (B)1Root of inferior square ViNamely:
Figure BDA0003321116020000085
c. to WiNormalization process
Figure BDA0003321116020000091
Then the process of the first step is carried out,
Figure BDA0003321116020000092
to determine the eigenvectors of the matrix, i.e. n1The weight of each element to the target layer a.
d. Consistency check
To confirm whether the above results are reasonable, a consistency check is required. CR is the consistency ratio and is calculated as follows:
Figure BDA0003321116020000093
Figure BDA0003321116020000094
in formula (7), λmaxFor the maximum feature root of the decision matrix B, RI is the average consistency index, and for the decision matrices of orders 1 to 9, RI values are shown in table 3.
TABLE 3
n 1 2 3 4 5 6 7 8 9
RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45
Generally, when CR is less than 0.10, the judgment matrix is considered to be consistent, and the weight distribution is convincing; otherwise, the parameters of the judgment matrix need to be improved and reanalyzed until the consistency requirement is met.
2.2 Objective weighting based on entropy weight method
Information entropy theory has its origin in thermodynamics and was originally introduced by shannon into information systems. The information entropy expresses the uncertainty degree of the value of the random variable and is used for describing the information quantity. The entropy weight method is an effective method for determining the index weight in the multi-index comprehensive evaluation problem, the information entropy of the index is obtained through calculation of the entropy weight method, the larger the information entropy value is, the larger the uncertainty is, and the smaller the weight is; the smaller the information entropy value, the smaller the uncertainty, and the larger the weight.
Assuming factor fr(r＝1，2，…，n1) Has an index of ars(s＝1，2，…，n2) All index layers are measured in m groupsData, including historical data and most recent measurement data at the time of evaluation. The entropy weight calculation process of the index layer is as follows:
2.2.1 normalization of raw data
Because the indexes and the sizes of the equipment health state evaluation are different, in order to unify the indexes, the evaluation indexes are normalized by adopting the relative degradation degree in fuzzy mathematics. The relative degradation degree is used for representing the degradation degree of the index actual data when compared with the attention state, and the value of the relative degradation degree is generally between 0 and 1.
For the index with smaller measurement value, the calculation formula is as follows (9):
Figure BDA0003321116020000101
for the index with the larger measurement value, the better, the calculation formula is as follows (10):
Figure BDA0003321116020000102
in formulae (9) and (10), k is 1,2, …, m, XrskIs a factor frMiddle index arsThe k-th measurement of (2), xrskThe result after normalization processing is obtained; xrs0Is a factor frMiddle index arsAn initial value of (1); xrs1Is a factor frMiddle index arsThe attention value of (1).
2.2.2 calculating index information entropy
Figure BDA0003321116020000103
Figure BDA0003321116020000104
Wherein m represents the number of sets of index measurement data; e.g. of the typersIs a factor frMiddle index arsThe entropy of information of (1). r isrsThe larger the index a is, the morersThe smaller the weight of (c).
2.2.3 calculating the objective weight of each index layer according to the information entropy
Figure BDA0003321116020000105
Figure BDA0003321116020000106
And is
Figure BDA0003321116020000107
Wherein n is2Representing factor frNumber of indexes contained, qrsIs a factor frSingle weight value of the s-th index (s-1, 2, …, n)2)；QrIs a factor frCorresponding to the index weight vector.
3. Residual service life of evaluation equipment (power grid overdue asset)
And determining the weights of the factor layer and the index layer by using an analytic hierarchy process and an entropy weight process respectively based on the comprehensive evaluation index system of the health state of the equipment, and calculating the corresponding probability value of the health state grade of the equipment by combining with a membership function. A health index calculation formula of United kingdom EA company is introduced to evaluate the residual service life of the equipment.
3.1 evaluation of health status of indicator layer
The equipment health state evaluation has fuzzy and uncertain properties, and the membership function in the fuzzy set theory can solve the fuzzy problem. Fuzzy comprehensive evaluation is an effective multi-factor decision method for comprehensively evaluating things influenced by various factors. Based on the method, the health state membership function is applied to evaluate the health state of the index layer.
The membership function indicates that if any element x in the domain U has mu (x) E [0,1] corresponding to the element x, mu is called a fuzzy set on U, and mu (x) is called a membership function of x to mu. The closer to 1 the degree of membership μ (x) is, the higher the degree to which x belongs to μ, and the closer to 0 μ (x) is, the lower the degree to which x belongs to μ.
In assessing device health, the universe of discourse U represents device health, x represents an index measurement, and μ represents a fuzzy set of device health, including μ1"Normal", mu2"Note", μ3"abnormal", μ4"Severe" 4 categories, μ (x) indicates the degree of membership of the index value to the 4 categories of health status. For example, when μ1(x) The closer to 1, the higher the degree of the index belonging to the normal state; mu.s1(x) The closer to 0, the lower the degree of the index belonging to the normal state.
Currently, determining membership functions relies mainly on empirical discrimination. Through a large amount of data research, it is found that a semi-trapezoidal membership function can be used in the evaluation of the health status of the power grid equipment, as shown in fig. 2.
In fig. 2, the abscissa represents the measured value of the evaluation index, and the ordinate represents the membership value of the measured value of the index corresponding to different health states.
The function formula corresponding to each health status grade in the membership function can be represented by the following formula, and the sum of the membership of each status is 1.
Figure BDA0003321116020000111
Figure BDA0003321116020000112
Figure BDA0003321116020000113
Figure BDA0003321116020000114
Wherein, muL(xrsk) (L ═ 1,2,3,4) denotes an index measurement value xrskMembership values corresponding to different health states.
Normalizing the latest measured value of each index in the same factor and then bringing the normalized value into the formula to obtain a membership matrix of the index of the factor, wherein the formula (19) is as follows:
Figure BDA0003321116020000121
wherein, UrRepresenting factor frA membership matrix of the lower index layer; mu.sL(xrs) Is represented by an index arsThe health status probability value calculated from the latest measured value of (a).
3.2 factor layer health status assessment
The health state probability value of the factor layer can be calculated by the weight and the probability value of the index layer. Firstly, the latest measured value of each index of the equipment is normalized, and the factor f is calculated according to the membership functionrMembership matrix U of lower indexr. Secondly, through all measured values of all indexes of the equipment, an index weight vector Q under the factor is calculated by utilizing an entropy weight methodr. Finally, the health state probability value G corresponding to the factor is obtained through weighting calculationr. I.e. the probability value of the health status of the factor layer is
Figure BDA0003321116020000122
As shown in equation (21):
Gr＝Qr*Ur＝(h1(Ar) h2(Ar) h3(Ar) h4(Ar)) (20)，
Figure BDA0003321116020000123
wherein Q isrRepresenting factor frA corresponding index layer weight matrix; u shaperRepresenting factor frA corresponding index layer membership matrix; grRepresenting the factor f obtained by the weighted calculationrThe health status of donkey.
3.3 the overall health state of the equipment is evaluated by combining the weights W of the factor layers obtained by the analytic hierarchy process, and the overall state evaluation result of the equipment is obtained by calculating by using the state probability matrix G of the factor layers, wherein the formula is as follows:
L＝W*G＝(mr(L1) mr(L2) mr(L3) mr(L4)) (22)，
wherein L represents a device health state probability matrix; mr (L)i) And indicating the corresponding health state probability value of the equipment. Calculating probability value mr (L) of the equipment in 4 types of health statesi) After that, the remaining useful life of the device was studied.
3.4 remaining useful life of computing device
The health index directly shows the current health state of the evaluation equipment in a numerical mode, and is a very intuitive and effective display mode. And (3) referring to relevant scoring concepts of evaluation guide rules such as Q/GDW 169 and 2008 oil-immersed transformer (reactor) state evaluation guide rules, DL/T1685 and 2017 oil-immersed transformer (reactor) state evaluation guide rules, and directly converting the result of the comprehensive evaluation model into a health index taking the score as a reference. And defining a basic score corresponding to each evaluation state, and multiplying the probability value of the equipment health state evaluation by the basic score to sum to obtain the current equipment health index. The health status of the equipment is listed in table 4.
TABLE 4
Health State of the Equipment (L) Is normal Attention is paid to Abnormality (S) Severe severity of disease
Health Index (HI) 1 4 6 8
Membership value mr(L1) mr(L2) mr(L3) mr(L4)
The equipment health index is:
HI＝mr(L1)×1+mr(L2)×4+mr(L3)×6+mr(L4)×8(23)，
wherein HI represents the health index of the equipment, and the value range is [0, 10 ]]Smaller values indicate better health of the device; otherwise, the health state of the equipment is worse; mr (L)i) And indicating the corresponding health state probability value of the equipment.
And introducing a health index calculation formula of the United kingdom EA company when the residual service life of the equipment is calculated. The formula is based on the equipment aging principle, reflects the change condition of the equipment health level index along with time, and is widely applied to health state evaluation of electric equipment in the United kingdom and North America at present. The health index calculation formula is as follows:
Figure BDA0003321116020000131
wherein, HI0Represents the initial health index of the equipment (generally takes a value of 0.5); HI represents the health index at the time of device evaluation; b represents an aging coefficient; t is1Indicating a completely new plant HI0The corresponding year is generally the equipment commissioning year; t is2Indicates the year corresponding to the HI to be calculated, and may be the current yearOr the next year. The value range of the health index is 0-10, the lower the value is, the better the health state of the equipment is, the general threshold value is 7, namely when the health index is 7, the health state of the equipment is considered to be very serious, and the equipment is not suitable for being used continuously.
The steps of calculating the residual service life of the equipment are as follows:
3.4.1 computing device life expectancy
Although the technical life (T) is specified when the equipment is delivered from a factory, the actual service life of the equipment cannot be truly reflected. Considering that the service life of the equipment is influenced by actual operation and the environment, an operation coefficient (f) is introducedL) And environmental coefficient (f)E) The technical life is corrected. Life expectancy (T) of the equipmentexp) The calculation formula is as follows:
Figure BDA0003321116020000141
wherein T represents the technical life of the equipment; f. ofLRepresenting the equipment operation coefficient; f. ofEAn environment coefficient representing an environment in which the device is located; t isexpIndicating the expected life of the device.
Taking a transformer as an example, the transformer operation coefficient can be expressed by a load coefficient. Generally, the load factor varies with the transformer load factor, which is the ratio of the average load carried by the transformer during operation to the rated capacity of the transformer
Figure BDA0003321116020000142
For reference, the corresponding relationship between the transformer load factor and the load factor is shown in table 5.
TABLE 5
Load factor of transformer Coefficient of load
0-40％ 1
40％-60％ 1.05
60％-70％ 1.1
70％-80％ 1.25
80％-150％ 1.6
Because the equipment is in different environments, factors such as the environmental pollution degree and the like have great influence on the safe operation of the equipment. For reference, the corresponding relationship between the environmental severity and the environmental coefficient is shown in table 6.
TABLE 6
Grade of environmental harshness Coefficient of environment
0 1
1 1
2 1.05
3 1.15
4 1.3
3.4.2 calculating aging factor
The aging coefficient B is calculated according to the formula (26):
Figure BDA0003321116020000143
wherein B represents the equipment aging coefficient; the health index of the scrapped equipment is expressed, and the value is generally HI-7; HI (high-intensity)0The initial health index of the equipment during operation is expressed and generally takes the value of HI0＝0.5；TexpIndicating the expected life of the equipment, i.e. the age from commissioning to scrapping.
3.4.3 calculating the remaining useful life
The remaining useful life EOL of a device is the time when the current health index of the device is estimated to be the health index at which it is scrapped. When the health state of the equipment is evaluated, once the health index of the equipment in the result is more than 6, the fault occurrence probability of the equipment is rapidly increased, and therefore, the health index threshold value is set to be 7 when the remaining service life of the equipment is evaluated. When the equipment health index reaches 7, measures need to be taken in time to ensure the safe and stable operation of the power grid system. The EOL calculation formula is shown in equation (27):
Figure BDA0003321116020000151
wherein EOL represents the remaining useful life of the device as calculated from the health index; HI represents a health index calculated by a comprehensive evaluation model; b denotes a device aging coefficient considering the operation coefficient and the environment coefficient.
3.5 remaining service life after technical improvement of equipment
An optimal technical transformation investment scheme can be calculated through a local technical transformation dynamic planning model year by year, so that the overall service life of the assets after technical transformation is maximum. On the premise that the expected asset service life X is determined, the calculation formula of the weighted average service life is as follows:
weighted average age (R) projected asset age-weighted average age
In the dynamic planning model, the estimated asset usable life is directly referred to the classified asset financial depreciation life standard, and the weighted average usable life R calculated by the method can be used for measuring the depreciation life which can be prolonged after the asset improvement is completed. However, the influence of the actual operation condition of the equipment on the service life of the equipment is not considered in the process, and the calculated weighted average service life can be further corrected from the health dimension of the equipment.
After the equipment is technically improved, the remaining service life T' of the equipment can be evaluated by constructing an equipment health state evaluation model. The difference between T' and R is compared to determine the weighted average remaining useful life of the planned total amount of the improvement, modified after all has been invested, i.e., the remaining useful life of the final device after the improvement. The determination method comprises the following steps:
the residual service life of the overdue assets of the power grid after technical transformation is min [ T', R ] (28),
wherein R represents the weighted remaining useful life of the evaluation after the local technical improvement is completed; t' represents the remaining useful life as assessed by the health of the device; and if T '< R, taking T' as the residual service life after the final equipment is technically improved, and otherwise, taking R.
3.5.1 modified weighted average remaining useful Life
When the equipment is technically improved, the remaining service life needs to be evaluated to determine a specific depreciation method, so that the depreciation of the equipment can be improved before the equipment is scrapped. The measurement data after the equipment technical modification is brought into a comprehensive evaluation model to obtain HI0And the residual service life T' after the equipment is technically improved is as follows:
Figure BDA0003321116020000161
wherein T' represents the remaining service life calculated by the health index after the equipment is technically improved; HI (high-intensity)0Representing a technically modified health index of the equipment calculated by the comprehensive evaluation model; b denotes a device aging coefficient considering the operation coefficient and the environment coefficient.
For the technically improved equipment, when the financial depreciation age is determined, the weighted average usable age calculated by referring to the asset assessment can be used, so that the financial depreciation age after the asset is technically improved is reasonably prolonged. But the asset evaluation does not consider the actual operation condition of the equipment, and the remaining service life after the equipment is technically improved can be influenced by the health condition of the equipment, so that the equipment is not depreciated and is scrapped in advance. Therefore, from the health state of the equipment, the remaining service life of the equipment after technical modification is evaluated, and the weighted average service life is corrected.
The modified weighted average may still be used for a period of min T', R (30),
wherein, R represents the weighted average service life of the asset evaluation calculation, and T' represents the remaining service life of the equipment after the equipment technology is improved and calculated by the comprehensive evaluation model of the health state of the equipment. If R < T', the health state of the equipment after technical modification is good, the equipment is sufficiently depreciated within the financial depreciation age limit, and the financial depreciation age limit after the equipment technical modification can be formulated by referring to R; if R > T ', the health state of the equipment after the equipment is technically improved is poor, the equipment can be scrapped before depreciation is carried out, and therefore the financial depreciation age after the equipment is technically improved is set by referring to T'.
As shown in fig. 3, the present solution further provides a power grid overdue asset value remodeling system 10, including:
the data acquisition unit 20 is used for acquiring various parameters of the overdue assets of the power grid for technical transformation;
the data analysis unit 30 is used for constructing a local technical transformation dynamic planning model for the power grid overdue assets year by year, and determining a technical transformation scheme for the power grid overdue assets and the weighted average usable life of the power grid overdue assets after technical transformation;
constructing a comprehensive evaluation index system of the health state of the overdue assets of the power grid based on the actual operation state of the current power grid equipment, and evaluating the remaining service life of the overdue assets of the power grid;
and calculating the remaining service life of the power grid overdue asset after technical transformation based on a power grid overdue asset health state comprehensive evaluation index system, and comparing the remaining service life with the weighted average service life of the power grid overdue asset after technical transformation to determine the remaining service life of the power grid overdue asset after technical transformation.
On the basis of the above method embodiments, the present embodiment further provides a computer-readable storage medium. The computer-readable storage medium is a program product for implementing the above-described identification method, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a device, such as a personal computer. However, the program product in this embodiment is not limited in this respect, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as JAvA, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The present invention will be further illustrated by the following specific examples
One transformer is currently put into operation in 1996 in a certain city company, the original value of the asset is 100 ten thousand, the technical life is 30 years, the financial depreciation age limit is 18 years, and the end 2014 is overdue. The technology is improved in 2015, the total investment of the technology improvement is estimated to be 120 ten thousand in five years, and the whole asset is replaced. Through past testing and experimentation, relevant operating data for the plant over 6 years of history has been obtained. It is necessary to determine a reasonable technical improvement scheme to reshape the asset value.
In order to meet the requirements that equipment can be upgraded every year and the overall service life of the equipment is the largest after the equipment is technically improved, the following dynamic planning model can be constructed.
The values of the known parameters in the model are as follows: after the aged asset is technically improved, the expected service life X is 18 years; the original value c of the overdue asset is 100 ten thousand yuan; the total investment amount b of the technical improvement plan is 120 ten thousand yuan; the planned technical improvement number m is 2.
According to the dynamic programming model, the following objective functions and constraint conditions are obtained:
Figure BDA0003321116020000181
Figure BDA0003321116020000191
and (4) bringing the known parameters into the model to obtain the optimal investment amount and the corresponding investment year.
And obtaining a calculation result through MATLAB software, wherein the optimal investment scheme is that 23.1 ten thousand yuan is invested in 2015 and 96.9 ten thousand yuan is invested in 2019. The 2020 estimated device weighted average age is
Figure BDA0003321116020000192
And if the expected usable life is 18 years according to the financial depreciation life, the weighted average usable life is 18-1.8-16.2 years, and the total usable life is 16.2+ 5-21.2 years after the equipment technology is modified. Table 7 is a technical reconstruction investment scenario data table.
TABLE 7
Figure BDA0003321116020000201
The whole technical improvement needs to invest 120 ten thousand yuan in 2015 to technically improve the equipment, and the whole service life of the technical improvement equipment is 18 years. Local technical improvement after the first technical improvement in 2015, the second technical improvement can be carried out in the rest years, and 4 schemes are provided. The technical improvement of the equipment in 2015 and 2019 requires investment of 23.1 ten thousand yuan and 96.9 ten thousand yuan respectively, the overall service life of the technical improvement equipment is 21.2 years, the overall service life of the technical improvement equipment exceeds that of other 3 local technical improvement schemes, and the local optimal solution is obtained.
Evaluating the residual service life of the equipment after technical transformation
The health state of the transformer is evaluated, and the remaining service life of the transformer after technical modification is predicted, and specific time nodes are shown in fig. 4.
1. Construction of comprehensive evaluation index system of health state of transformer
Table 8 shows a comprehensive evaluation index system for the health status of the transformer.
TABLE 8
Figure BDA0003321116020000211
2. Calculating the membership degree of the index layer
The transformer has the defects of body oil chromatographic data abnormality for many times since the transformer is put into operation in 1996, and is subjected to vacuum oil filtering treatment for many times. Several test data of the transformer since commissioning are obtained from the power company, specifically including historical electrical test reports and insulating oil detection reports. Specific data are shown in table 9.
TABLE 9
Figure BDA0003321116020000221
In the related research of the evaluation of the health state of the transformer, the parameter values of the membership function are generally determined after the experience of operators and a large number of field tests and verifications are combined. Referring to the results of the related studies, the boundary parameters of the membership function are defined as b being 0.3, c being 0.45, d being 0.5, e being 0.75, f being 0.8, and g being 0.95.
And carrying out normalization processing on the measured values of the indexes in the table according to the actual operation condition of the transformer. The results of 2020 testing are normalized and substituted into the membership function to obtain the membership of each index in health status as shown in table 10.
Watch 10
Figure BDA0003321116020000231
3. Calculating weights
And (4) after the 7 groups of measured values of the transformer are subjected to normalization processing, calculating the weight of the index layer by using an entropy weight method. The factor layer weights were determined by a hierarchical analysis method, and the obtained results are shown in table 11.
TABLE 11
Figure BDA0003321116020000232
4. Calculating health index
And calculating to obtain a state evaluation result of the equipment index layer through the index membership and the weight, obtaining probability values of normal, attention, abnormality and severity of the equipment by combining the weight of the factor layer, and converting the qualitative state of the equipment into a health index value by adopting the following formula. Table 12 is a transformer state distribution column.
TABLE 12
Device state (L) Is normal Attention is paid to Abnormality (S) Severe severity of disease
Health Index (HI) 1 4 6 8
Probability value of health status 0.917481 0.082519 0 0
HI＝1×0.917481+4×0.082519＝1.2476
The health index HI of the transformer 2020 is calculated to be 1.2476.
5. Remaining service life after technical improvement of equipment
According to the standard that the technical life of the transformer is 30 years and the environmental coefficient and the load coefficient are 1, the aging coefficient of the computing equipment is as follows:
Figure BDA0003321116020000241
the remaining service life of the transformer after technical modification is as follows:
Figure BDA0003321116020000242
namely, the equipment can be used for 19.61 years after being technically improved, and then the health index of the equipment reaches 7 and is in a serious state, and corresponding measures are needed to ensure the operation safety.
6. Modified weighted average remaining useful life
The residual service life + min 19.616.2 (year) after the final equipment technology transformation
After evaluation, the depreciation age of the transformer after technical modification is determined to be 16.2 years.
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention, and it will be obvious to those skilled in the art that other variations or modifications may be made on the basis of the above description, and all embodiments may not be exhaustive, and all obvious variations or modifications may be included within the scope of the present invention.

Claims (10)

1. A power grid overdue asset value remodeling method is characterized by comprising the following steps:
constructing a local technical transformation dynamic planning model for the overdue assets of the power grid year by year, and determining a technical transformation scheme for the overdue assets of the power grid and a weighted average usable life of the overdue assets of the power grid after technical transformation;
constructing a comprehensive evaluation index system of the health state of the overdue assets of the power grid based on the actual operation state of the current power grid equipment, and evaluating the remaining service life of the overdue assets of the power grid;
and calculating the remaining service life of the power grid overdue asset after technical transformation based on a power grid overdue asset health state comprehensive evaluation index system, and comparing the remaining service life with the weighted average service life of the power grid overdue asset after technical transformation to determine the remaining service life of the power grid overdue asset after technical transformation.
2. The method for reconstructing the value of the overdue asset of the power grid according to claim 1, wherein the dynamic planning model for local technical reconstruction of the overdue asset of the power grid year by year is shown as a formula (1) and a formula (2):
an objective function:
Figure FDA0003321116010000011
constraint conditions are as follows:
Figure FDA0003321116010000012
in the formulas (1) and (2), X represents the predicted service life of the aged asset after technical transformation; w is aiRepresenting an investment amount of the ith technical improvement; t is tiIs a variable between 0 and 1, and indicates whether the technology modification is carried out for the ith time, if the technology modification is carried out for the ith time,then tiWhen the ith time is not technically modified, t is 1i0; (j-i) represents the age in which the renewed modified capital was put into use relative to the estimated year; b is total investment of technical improvement; n is1Representing the actual years elapsed before the evaluation year; y isiIndicating a net worth of assets at the end of the ith year; z is a radical ofiIndicating the original asset value of the ith technical reconstruction replacement equipment; m represents the total number of planned technical modifications; n represents the completion time of the technical transformation plan; j denotes the year of evaluation.
3. The method for reconstructing the value of the power grid aged asset according to claim 1, wherein the constructing of the comprehensive evaluation index system of the health state of the power grid aged asset comprises determining an evaluation index system and weights of all levels in the system based on the type of equipment.
4. The power grid overdue asset value remodeling method according to claim 3, wherein each level in the system comprises a factor layer, an index layer and a device health status level; the factor layer weight is determined based on an analytic hierarchy process; and the weight of the index layer is determined by applying an entropy weight method based on historical data obtained by evaluating and measuring the equipment state.
5. The power grid overdue asset value remodeling method according to claim 4, wherein the corresponding value of the equipment health state grade is obtained based on factor layer weight and index layer weight and by combining a membership function.
6. The grid aging asset value remodeling method according to claim 5, wherein the corresponding value of the equipment health status grade is obtained by equation (24),
Figure FDA0003321116010000021
in the formula (24), HI0An initial health index representing the device; HI represents the health index at the time of device evaluation; b representsAn aging factor; t is1Indicating a completely new plant HI0The corresponding year is generally the equipment commissioning year; t is2Indicating the year corresponding to the HI to be calculated, which may be the current year or a future year.
7. The method for reconstructing the value of the overdue asset of the power grid according to claim 6, wherein the step of obtaining the remaining service life of the overdue asset of the power grid based on the comprehensive health state evaluation index system of the overdue asset of the power grid comprises the following steps:
calculating the expected life of the overdue asset of the power grid;
calculating an aging coefficient of the power grid overdue asset;
and obtaining the residual service life T' of the overdue asset of the power grid.
8. The method for remodeling of value of an aged asset of a power grid according to claim 7, wherein determining the remaining service life of the aged asset of the power grid after technical transformation comprises:
calculating a weighted average remaining useful life R of the power grid overdue asset based on the predicted useful life of the power grid overdue asset;
comparing the weighted average remaining usable life R of the power grid overdue asset with the remaining usable life T' of the power grid overdue asset, determining the remaining usable life of the power grid overdue asset after technical transformation, as shown in formula (28),
the residual service life of the overdue assets of the power grid after technical transformation is min [ T', R ] (28),
in the formula (28), R represents the weighted average remaining usable life of the power grid overdue assets after technical transformation; t' represents the remaining usable life obtained through a power grid overdue asset health state index system; if T 'is less than R, finally taking T' from the residual service life of the overdue asset of the power grid after technical transformation; otherwise, taking R.
9. A power grid overdue asset value remodeling system is characterized by comprising:
the data acquisition unit is used for acquiring various parameters of the overdue assets of the power grid subjected to technical transformation;
the data analysis unit is used for constructing a local technical transformation dynamic planning model for the power grid overdue assets year by year, and determining a technical transformation scheme for the power grid overdue assets and the weighted average usable life of the power grid overdue assets after technical transformation;
constructing a comprehensive evaluation index system of the health state of the overdue assets of the power grid based on the actual operation state of the current power grid equipment, and evaluating the remaining service life of the overdue assets of the power grid;
and calculating the remaining service life of the power grid overdue asset after technical transformation based on a power grid overdue asset health state comprehensive evaluation index system, and comparing the remaining service life with the weighted average service life of the power grid overdue asset after technical transformation to determine the remaining service life of the power grid overdue asset after technical transformation.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
CN202111246823.9A 2021-10-26 2021-10-26 Power grid overdue asset value remodeling method, system and storage medium Pending CN113947254A (en)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105488572A (en) * 2015-11-23 2016-04-13 中国电力科学研究院 Health state evaluation method of power distribution equipment
CN110633902A (en) * 2019-09-04 2019-12-31 国网天津市电力公司 Power grid investment benefit evaluation method suitable for power transmission and distribution price reformation
CN112132446A (en) * 2020-09-18 2020-12-25 国网浙江省电力有限公司经济技术研究院 Power grid technical improvement investment allocation method based on Gini coefficient theory
CN113077124A (en) * 2021-03-15 2021-07-06 国家电网有限公司 Method for evaluating remaining usable life of power grid aged equipment after technical modification
CN113077155A (en) * 2021-04-07 2021-07-06 国家电网有限公司 Big data situation perception-based power production technical improvement project evaluation model

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105488572A (en) * 2015-11-23 2016-04-13 中国电力科学研究院 Health state evaluation method of power distribution equipment
CN110633902A (en) * 2019-09-04 2019-12-31 国网天津市电力公司 Power grid investment benefit evaluation method suitable for power transmission and distribution price reformation
CN112132446A (en) * 2020-09-18 2020-12-25 国网浙江省电力有限公司经济技术研究院 Power grid technical improvement investment allocation method based on Gini coefficient theory
CN113077124A (en) * 2021-03-15 2021-07-06 国家电网有限公司 Method for evaluating remaining usable life of power grid aged equipment after technical modification
CN113077155A (en) * 2021-04-07 2021-07-06 国家电网有限公司 Big data situation perception-based power production technical improvement project evaluation model

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
薛聪等: "电改背景下的资产退出机制研究", 《财会学习》 *
许李婷: "电网企业逾龄资产探讨与研究", 《商讯》 *

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